The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
Data analysts often perform analysis of a large collection of data items, such as data relating to the medical field, the financial industry, the real estate market, and the like. In many instances, the amount of raw data about data items (also referred to as “inventory”) can be massive and dynamically increasing all the time. For example, such data may be updated in large volumes and/or numerous times in a day. Therefore, in addition to metadata that captures relatively stable aspects of the inventory, a huge amount of raw data may be accumulated over a particular period of time.
While inventory can possibly be analyzed based on the raw data, it is often difficult to make sense of the raw data, metadata, or related computations. This problem is drastically compounded when analyzing a large collection of inventory. Thus, an analyst often is forced to rely on inexact hunches, experience, and/or cumbersome spreadsheets to identify trends, diagnose problems, and/or otherwise evaluate the inventory.